Deep Convolution Neural Networks, or Deep CNN is the most core of the remarkable development in the field of Deep Learning. Though the CNN has been employed to solve character recognition problems in 1990s, it is not until recently that the CNN has become widespread in Machine Learning. For example, in 2012, the CNN significantly outperformed its competitors in an annual software contest, the ImageNet Large Scale Visual Recognition Challenge, and won the contest. After that, the CNN has become a very useful tool in the field of machine learning.
Recently, the CNNs are widely used in a field of an autonomous driving. The CNNs may perform an object detection, a semantic segmentation and a free space detection by processing its own inputted image in the field of the autonomous driving. Herein, for the CNNs to perform those functions, enormous size of training sets are necessary.
An obstacle of the approach shown above, using CNNs in the field of the autonomous driving, is that the training sets cost a lot. Further, in order to generate the training sets, although training images can be acquired automatically, heir corresponding GTs should be generated manually by people, resulting in much cost.
An alternative approach to overcome said obstacle is a virtual driving. In a virtual world simulated by a programmed computer, both of the training images and their corresponding GTs can be acquired automatically, resulting in lower cost.
However, this alternative approach has another obstacle that the training images acquired in the virtual world is different from images of real world, resulting in lower credibility on the CNNs trained in the virtual world.